class_docstr: Pseudo-boolean functions taken from the IOHexperimenter project, adapted for minimization
  
  Parameters
  ----------
  fid: int
      function number (for a list of functions, please see the
      `documentation <https://www.sciencedirect.com/science/article/abs/pii/S1568494619308099>>`_
  iid: int
      the instance of the function, specifies the transformations in variable and objective space
  dim: int
      The dimensionality of the problem. For function 21 and 23 (N-queens), this needs to be a perfect square
  instrumentation: str
      How the paramerterization is handled. Either "Softmax", in which case the optimizers will work on probabilities
      (so the same parametrization may actually lead to different values), or "Ordered". With this one the optimizer
      will handle a value, which will lead to a 0 if it is negative, and 1 if it is positive.
      This way it is no more stochastic, but the problem becomes very non-continuous
  
  Notes
  -----
  The IOHexperimenter is part of the IOHprofiler project (IOHprofiler.github.io)
  
  References
  -----
  For a full description of the available problems and used transformation methods,
  please see "Benchmarking discrete optimization heuristics with IOHprofiler" by C. Doerr et al.
code: def _evaluation_internal(self, x: np.ndarray) -> float:
      assert len(x) == self.f_internal.number_of_variables
      return -float(self.f_internal(x))
  
file_path: nevergrad/nevergrad/functions/iohprofiler/core.py
imports: import numpy
  import nevergrad
  from ..None import base
label_desc: Use image node_id 2 for calling the PBOFunction obj's underlying member method code with example usage: obj._evaluation_internal(x) and returns: unknown